The spatial noise generates a spurious image which degrades the wanted image or even renders it unusable: assuming that the scene observed by the sensor is an image of uniformly average luminance, the sensor provides a non-uniform image, which is unacceptable if this non-uniformity exceeds a certain degree; furthermore, the observed image depends on the level of this uniform luminance, which renders this noise yet more problematic since the difference in the behavior of two pixels depends on the luminance that they receive.
The behavior of the pixels differs from one pixel to another not only with regard to the signal level produced for a reference luminance level, but also with respect to the growth slope and general appearance (for simplicity: the curvature) of the response curve of the pixel according to the luminance.
To minimize the spatial noise generated in this way in a matrix sensor, it has already been proposed to record the output signal levels from the various pixels for a uniform image of given luminance and to individually offset the signal level of each pixel so that all the pixels are brought to one and the same reference (first order correction). It has also been proposed to record the levels for two uniform luminance levels, in order to correct not only the level offset but also the slope of variation (second order correction).
These methods require manual calibration based on one or two uniform images exhibiting reference luminances, which is awkward; moreover, this calibration must be redone if the spatial noise drifts over time.
Finally, it has been proposed to perform corrective calculations of each of the dots of the collected image, based on the observation of a large number of successive images, by applying the hypothesis that the statistical average and the statistical variance of the light levels received by a pixel is the same for all the pixels because of the diversity of the images received over time. Thus, the average of the received signals is calculated in time for each pixel and a correction is performed on the current signal from the pixel to offset the current level by a value corresponding to the deviation between the average detected for this pixel and a reference average value common to all the pixels. This brings the average level of all the signals to the same reference value.
Similarly, the variance is calculated for each pixel over a large number of images, this variance being somewhat representative of an approximation of the slope of the curve of variation of the signal level as a function of the luminance, and a gain correction is applied to the current signal variations, the correction being the deviation between the calculated variance and a reference variance common to all the pixels. This brings the slope of variation of each pixel to one and the same reference value.
This solution is very advantageous since it requires no calibration based on patterns.
However, these calculations are very cumbersome since they require a large number of images to be collected, all of them to be stored, average calculations to be done for each pixel over this large number of images, and variance calculations on each pixel. In practice, this can be performed only by a powerful computer, on series of prestored images. The image could not be collected and processed directly in the photographing camera. Consequently, although this solution can theoretically be used to process images off line, it is not at all applicable for an instantaneous shot.
The invention proposes a solution to overcome this difficulty.